Developer Tools / Code Assistant

Empirical Lyapunov Stability: Growth-Ratio Energy Functions as Leading Indicators of Agent Task Failure

A research article describing an empirical runtime-monitoring approach for multi-turn LLM agents, backed by 3,175 runs across four benchmarks. It also points to an open-source implementation, state-harness, with Rust/Python support, LangGraph and CrewAI adapters, CLI tooling, and OpenTelemetry export.

Clear26/30
Useful24/30
Specific14/20
Complete10/20
Empirical Lyapunov Stability: Growth-Ratio Energy Functions as Leading Indicators of Agent Task Failure screenshot

Why it was accepted

The page is clearly about an AI-adjacent developer tool and runtime safety method for LLM agents, with strong evidence of a real implementation. The snapshot shows a concrete open-source library, installation command, integrations, benchmarks, and evaluation results across multiple model families and agent workloads. That is enough for a useful public listing.

Weakness

The page reads as a paper/article first, so a visitor still cannot quickly tell the product’s day-to-day workflow, key API surface, or how state-harness is structured in practice. The crawl also does not show docs, screenshots, release status, licensing, or a quick-start example beyond the pip install line.

Review status

39 days ago #1932 ↓ -1

Last evaluated 39 days ago. Current rank #1932. Down 1 spot in the rankings.

Score history

74

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